quantile normalization on pandas dataframe

One thing worth noticing is that both ayhan and shawn's code use the smaller rank mean for ties, but if you use R package processcore's normalize.quantiles() , it would use the mean of rank means for ties.

Using the above example:

> df

   C1  C2  C3
A   5   4   3
B   2   1   4
C   3   4   6
D   4   2   8

> normalize.quantiles(as.matrix(df))

         C1        C2        C3
A  5.666667  5.166667  2.000000
B  2.000000  2.000000  3.000000
C  3.000000  5.166667  4.666667
D  4.666667  3.000000  5.666667

Ok I implemented the method myself of relatively high efficiency.

After finishing, this logic seems kind of easy but, anyway, I decided to post it here for any one feels confused like I was when I couldn't googled the available code.

The code is in github: Quantile Normalize

Using the example dataset from Wikipedia article:

df = pd.DataFrame({'C1': {'A': 5, 'B': 2, 'C': 3, 'D': 4},
                   'C2': {'A': 4, 'B': 1, 'C': 4, 'D': 2},
                   'C3': {'A': 3, 'B': 4, 'C': 6, 'D': 8}})

df
Out: 
   C1  C2  C3
A   5   4   3
B   2   1   4
C   3   4   6
D   4   2   8

For each rank, the mean value can be calculated with the following:

rank_mean = df.stack().groupby(df.rank(method='first').stack().astype(int)).mean()

rank_mean
Out: 
1    2.000000
2    3.000000
3    4.666667
4    5.666667
dtype: float64

Then the resulting Series, rank_mean, can be used as a mapping for the ranks to get the normalized results:

df.rank(method='min').stack().astype(int).map(rank_mean).unstack()
Out: 
         C1        C2        C3
A  5.666667  4.666667  2.000000
B  2.000000  2.000000  3.000000
C  3.000000  4.666667  4.666667
D  4.666667  3.000000  5.666667